The parent grant relies heavily upon proteomics as a means to accomplish our objectives and requires the use of analytical software developed in my lab, called CompPASS. CompPASS allows for rapid and reliable determination of high-confidence candidate interacting proteins (HCIPs) from amidst the many hundreds of identified proteins from typical mass spectral analyses of parallel protein complexes. In GM054137, Aim 1 proposes to systematically identify substrates of Dubs using catalytic trap mutants. In Aim 2 employs proteomics to explore Dub function. These aims require the computational resources of CompPASS, both in terms of its current implementation and also involving the further development of new bioinformatic and analytical tools which will be incorporated into the software suite (as proposed in this grant). This work would be greatly facilitated by the addition of new and more powerful computational resources. Furthermore, we are currently in the process of integrating CompPASS with existing mass spectral processing software which will create a continuum of services from the acquisition of MS/MS data through the CompPASS-based analysis of likely interacting proteins. We are also working to create a fully capable on-line version of CompPASS to facilitate the analysis of proteomic datasets obtained by other investigators without the need for our intervention (an initial, small scale version can be found online now;see Sowa et al., 2009). Currently, all analysis and data storage is performed using a desktop 4 year old Linux workstation with two Intel(R) Pentium(R) 4 3.2 GHz processors and a total of 2 GB of RAM and 1 TB of non-RAID storage space. While sufficient to develop our CompPASS platform, this computer system is now inadequate. Therefore, we are seeking additional funds to purchase a 12 processors (6 node) computational cluster as well as an 8TB data server and an additional 16 TB flexible storage system, both employing redundant data storage. With the implementation of this computational infrastructure, we will be able to meet the goals described in this grant, surpass these goals through increased throughput of data from tangentially derived proteomic investigations, and provide a robust and reliable computational platform for future efforts in my lab, those of my collaborators, and for the scientific community in general. PUBLIC HEALTH RELEVANCE: The parent grant seeks to employ systematic proteomics to elucidate the substrates and targets of a large family of deubiquitinating enzymes, an endeavor that has been strengthened by our development of a new proteomics software platform called CompPASS. This competing supplement seeks to obtain funding to help build computational and informatic resources that will allow use to significantly expand the CompPASS platform. Through these funds we will upgrade our computational infrastructure and thus allow for increases in the throughput of data processing and analysis, reliability of data storage, and provide a platform for the dissemination of these data and software to the scientific community as a whole.